docs: def convert_binarized_mnist(directory, output_directory,
                              output_filename='binarized_mnist.hdf5'):
      """Converts the binarized MNIST dataset to HDF5.
  
      Converts the binarized MNIST dataset used in R. Salakhutdinov's DBN
      paper [DBN] to an HDF5 dataset compatible with
      :class:`fuel.datasets.BinarizedMNIST`. The converted dataset is
      saved as 'binarized_mnist.hdf5'.
  
      This method assumes the existence of the files
      `binarized_mnist_{train,valid,test}.amat`, which are accessible
      through Hugo Larochelle's website [HUGO].
  
      .. [DBN] Ruslan Salakhutdinov and Iain Murray, *On the Quantitative
         Analysis of Deep Belief Networks*, Proceedings of the 25th
         international conference on Machine learning, 2008, pp. 872-879.
  
      Parameters
      ----------
      directory : str
          Directory in which input files reside.
      output_directory : str
          Directory in which to save the converted dataset.
      output_filename : str, optional
          Name of the saved dataset. Defaults to 'binarized_mnist.hdf5'.
  
      Returns
      -------
      output_paths : tuple of str
          Single-element tuple containing the path to the converted dataset.
  
      """
      output_path = os.path.join(output_directory, output_filename)
      h5file = h5py.File(output_path, mode='w')
  
      train_set = numpy.loadtxt(
          os.path.join(directory, TRAIN_FILE)).reshape(
              (-1, 1, 28, 28)).astype('uint8')
      valid_set = numpy.loadtxt(
          os.path.join(directory, VALID_FILE)).reshape(
              (-1, 1, 28, 28)).astype('uint8')
      test_set = numpy.loadtxt(
          os.path.join(directory, TEST_FILE)).reshape(
              (-1, 1, 28, 28)).astype('uint8')
      data = (('train', 'features', train_set),
              ('valid', 'features', valid_set),
              ('test', 'features', test_set))
      fill_hdf5_file(h5file, data)
      for i, label in enumerate(('batch', 'channel', 'height', 'width')):
          h5file['features'].dims[i].label = label
  
      h5file.flush()
      h5file.close()
  
      return (output_path,)